Using a methodology similar to the one developed by [1]}, HedgeMO chooses the next `\(Q\) -batch' of test points from the history of the candidates nominated by all of the acquisition functions. Rewards are calculated for each acquisition function from the surrogate surface for the entire history of the nominated points by the \(L\)  acquisition functions. The rewards are then normalized to scale them to the same range for each objective. This step is fundamentally important because it prevents biasing the probability of any objective. This type of bias, of course, cannot occur in single objective problems. The rewards for different objectives \(k\)  are then summed and the probability, \(p^l\) , of choosing a nominee from a specific acquisition function is calculated using step 6 in alg:mohedge.
For a `\(Q\) -batch' of tests points, the test points are chosen \(Q\)  times.
